Colorectal cancer is one of the leading causes of cancer-related deaths. Patient screening can reduce colon cancer by facilitating early detection and removal of pre-cancerous polyps. Colonoscopy is considered to have the highest diagnostic performance for screening colon cancer; however, colonoscopy also has a high cost, risk of complications and incidents of patient non-compliance. A minimally invasive alternative procedure, called computed tomography colonography (CTC) or “virtual colonoscopy,” is expected to be more cost effective and to involve a lower risk of complications than traditional colonoscopy.
Proper bowl preparation is considered essential for confident detection of colorectal lesions using CTC. This preparation traditionally includes cathartic cleansing of a patient's colon, because residual material in the colon reduces the sensitivity of CTC by imitating polyps. However, cathartic cleansing usually involves administering a laxative. Such cleansings are uncomfortable for the patient, and some residual material remains in the colon, even after such a cleansing. Orally-administered radio-opaque (or high X-ray opacity) contrast agents, such as dilute barium, can be used to opacify residual fluid and stool, so these opacified (“tagged”) materials can be identified and distinguished from polyps or other soft tissues. Procedures that use such tagging are commonly referred to as “fecal tagging CTC” (ftCTC).
Interpreting a large number of ftCTC screening cases can be time-consuming for a radiologist, who may grow weary of the task and occasionally miss small polyps or even subtle cancers. Automated image processing (“computer-aided detection” (CAD)) tools can be used to rapidly point out suspicious lesions to radiologists. However, in ftCTC, automated image processing is complicated by an effect commonly known as pseudo-enhancement (PEH), which is an atrifactual increase in the observed X-ray opacity (radio density) of tissues due to the presence of a near-by high radio density tagging agent.
In computed tomography (CT), the internals of an object, such as a human body, are imaged by taking X-ray measurements, yielding data that represents the object as many tightly packed cubes (“voxels”). The radio density of each voxel is calculated by taking the X-ray measurements through the object from a large number of perspectives. A computer digitally processes the X-ray measurements and generates data that represents a three-dimensional model of the object, including the internals of the object. Essentially, the computer “stacks” a series of “slices” of the object to create the model. The data can then be analyzed by a CAD tool. Alternatively or in addition, the data can be used to generate a three-dimensional display or for some other purpose.
The radio density (also called the “CT attenuation” or “CT number”) of each voxel is represented by a numeric value along an arbitrary scale (the Hounsfield scale), in which −1,000 represents the radio density of air, and +1,000 represents the radio density of bone. Air causes very little X-ray attenuation and is typically depicted in black on X-ray films, in CT images, etc., whereas bone greatly attenuates X-rays and is typically depicted in white on these films and images. Fat has a radio density of about −120 Hounsfield Units (HU), and muscle has a radio density of about +40 HU. Water is defined as having a radio density of 0 (zero) HU.
Intermediate amounts of CT attenuation are usually depicted by shades of gray in CT images. Because the human eye is unable to distinguish among 2000 shades of grey (representing HU values between +1,000 and −1,000), a radiographer selects a range of CT attenuations that is of interest (i.e., a range of HU values, known as a “window”), and all the CT attenuations within this range are spread over an available gray scale, such as 256 shades of gray. This mapping of a range of CT attenuations to shades of gray is known as “windowing.” The center of the range is known as the “window level.” Materials having radio densities higher than the top of the window are depicted in white, whereas materials having radio densities lower than the bottom of the window are depicted in black.
Windowing facilitates distinguishing between tissues having similar radio densities. For example, to image an area of a body, such as the mediastinum or the abdomen, in which many tissues have similar radio densities, a narrow range of CT attenuations is selected, and these CT attenuations are spread over the available shades of gray. Consequently, two tissues with only a small difference between their radio densities are ascribed separate shades of gray and can, therefore, be differentiated.
CAD tools identify polyps of interest based on shape. These polyps occur on the inside wall of the colon. Thus, to facilitate automatic polyp identification, a CAD system should receive data representing an extracted colon, but not other structures (such as a small bowel or lung base), because polyps or polyp-like features in these other structures can lead to false positive (FP) diagnoses. To limit the structures that are considered by a CAD system, the colon (and no other structures) should be extracted from CT image data. Extracting the colon involves identifying a colonic lumen. A “lumen” is a space inside any tubular structure in a body, such as an intestine, artery or vein. Because polyps occur on the inside wall of the colon, the colonic lumen can be used to extract the colonic wall from the CT image data.
Unfortunately, a patient's colon may not be fully distended when the CT image data is collected. That is, portions of the colon may be collapsed or may be filled with tagged material. In this case, the CT image data may contain several, sometimes many, disconnected lumen-like structures, some of which may be undesirable to include in an extracted colon.
Thus, tagging and PEH present problems for the automated extraction of the colon, which is an important part of any automated CAD scheme for CTC. Even if tagging is not used, fully automated colon extraction is a challenging problem in cases where the colonic lumen is split into multiple disconnected components, some of which may be separated from each other in distance by collapsed regions. Although visible regions of a colonic lumen can be reconnected over the collapsed segments of the colon, pieces of small bowel could be inadvertently included in the extracted region.
The presence of tagging can further complicate colon extraction, because thin walls between the colon and small bowel may become invisible in the ftCTC data due to PEH, which can result in complex networks of interconnected lumen paths between the colon and small bowel. In ftCTC, the colon is also more often connected to the small bowel through an open ileocecal valve than in CTC without tagging, because the opacified fluid at the ileocecal valve facilitates tracking the colonic lumen directly into the small bowel. Furthermore, osseous structures and tagged materials have similar CT attenuation values in ftCTC, and differentiating these materials may be challenging in cases where tagged regions and osseous structures appear to be directly connected because of a partial-volume effect and PEH.